Cazandra Aporbo Cazzy-Aporbo

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║  A LIFE IN MISMATCHED PASTELS                                                  ║
║  Every color misaligns just enough to make meaning                             ║
║  Looks accidental—until you see the pattern                                    ║
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FDF2F8 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
FFE0F5 ░░░░░░░░ 2022: Lab coat meets unexpected pastels ░░░░░░░░░░░
D4FFE4 ░░░░░░░░ Where pink met mint met purpose ░░░░░░░░░░░░░░░░░░░░

Name: Cazzy A.
Current Role: Head of Data @ FoXX Health
Background: Quality Control Scientist → Ethical AI
Trajectory: Former lab scientist who traded pipettes for Python

Education: 
  - MS Data Science (University of Denver)
  - BS Integrative Biology & Chemistry (OSU Cascades)
  - AI in Healthcare Certificate (Johns Hopkins, 2025)
  
Mission: Building AI that addresses healthcare inequities for women
Specialty: Pattern discovery in distribution tails & bias detection
Philosophy: Every model must be validated, evidence-based & production-ready
Approach: Effectiveness + Attractiveness + Impact = Excellence

# My ideal palette: Mismatched pastels that shouldn't work but do

I started in a lab coat, where I learned that good science means obsessing over validation and reproducibility. Turns out, those habits translate pretty well to machine learning.

I’m here to help teams build ethical & sustainable systems. I like the weird edges of data: tails, drift, the places fairness breaks. I bridge research and production. I say “no” when the data can’t support the claim. Bring me the messy dataset you’ve been avoiding; I’ll tell you what the tails are saying, and we’ll make it useful together.

If you care less about hype and more about calibration curves, we’ll get along. I like turning messy data into useful, fair systems—models that explain themselves, pass their audits, and still look good in a dashboard. If you’re curious about outliers, tail behavior, and pushing code that doesn’t quietly exclude half the population, say hi.

D4FFE4 ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒
93C5FD ▒▒▒▒▒▒▒▒ 2019: Data Science emerges ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒
FFCCE5 ▒▒▒▒▒▒▒▒ 30% reduction in errors ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒

Where I learned that
reproducibility is everything

Discovered Python doesn't
require safety goggles

Promoted twice
Data is my love language

Started fixing bias in
medical algorithms

Now designing
equitable healthcare AI

A7F3D0 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓
FFCCE5 ▓▓▓▓▓▓▓▓ 2024: Lead Data Scientist ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓
93C5FD ▓▓▓▓▓▓▓▓ Built ML platform from scratch ▓▓▓▓▓▓▓▓▓▓▓▓▓▓

def career_acceleration():
    """
    The gradient mismatches intentionally.
    Knowledge compounds in unexpected colors.
    """
    timeline = {
        "2024_Q1": "Lead Data Scientist",
        "2024_Q2": "Architected frameworks",
        "2024_Q3": "AI Engineer", 
        "2025": "Head of Data",
        "gradient": "exponential",
        "palette": "Always mismatched"
    }
    return "Where patterns emerge from chaos"

SERENDIPITY FINDER [MY SIGNATURE]

%%{init: {'theme': 'base'}}%%
graph TD
    A[Dataset] -->|r = 0.06| B[Everyone: No Pattern]
    A -->|But...| C[Me: Check the Extremes]
    C -->|Top 5%| D[r = 0.85!]
    C -->|Bottom 5%| E[r = 0.85!]
    D --> F[PATTERN HIDDEN IN EXTREMES]
    E --> F
    F --> G[Being evaluated for drug safety & financial risk]
    
    style A fill:#FFE0F5,stroke:#D4FFE4
    style C fill:#D4FFE4,stroke:#93C5FD
    style D fill:#93C5FD,stroke:#FFCCE5
    style F fill:#A7F3D0,stroke:#E6E0FF
    style G fill:#34D399,stroke:#FFE0F5

PROJECT MATRIX

Velvet Python

Coverage in pastels:
░░░░░ Hello (pink)
▒▒▒▒▒ Examples (mint)
▓▓▓▓▓ Tests (yellow)
█████ 94% (lavender)

Bridging the gap between "Hello World" and production code.
Every pattern benchmarked.

Mochi-Moo AI

147 shades of pastel:
░░░░░ Whisper (blush)
▒▒▒▒▒ Think (butter)
▓▓▓▓▓ Create (lilac)
█████ Feel (mint)

An AI assistant that adapts its thinking style.
Built with 147 shades of pastel.

CloudPoof Omega

Infrastructure softness:
░░░░░ Concept (mint)
▒▒▒▒▒ Build (peach)
▓▓▓▓▓ Deploy (sky)
█████ 12ms (sage)

Infrastructure that composes itself.
In mismatched pastels.

MACHINE LEARNING & AI

░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░

Machine Learning & AI

The stuff that actually ships to production

DATA SCIENCE & VISUALIZATION

▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒

Data Science & Visualization

Where math meets aesthetics

CLOUD & INFRASTRUCTURE

▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓

Cloud & Data Engineering

Because models need homes too

ADDITIONAL EXPLORATIONS

████████████████████████████████████████████████████████████████████

Programming Explorations

Because learning new languages keeps me curious

Statistics & QC

From measuring chemical reactions to measuring algorithmic bias.
The lab coat is gone but the hypothesis testing remains.

View Advanced Technical Portfolio

_{Comprehensive technical portfolio showcasing systems architecture, ML engineering, and infrastructure expertise}

View Technical Skills Portfolio

_{Full stack engineering excellence with production-grade implementations}

THE GRADIENT OF HARM:
░░░░░░░░░░░░░░░░░░░░░░░░░░░ Clinical trials exclude women
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 8/10 drugs affect women differently
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 50% higher misdiagnosis rate
████████████████████████████ Real people harmed daily

MY INTERVENTION (IN MISMATCHED PASTELS):
████████████████████████████ Detect bias (pink on mint)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Balance data (blue on blush)
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ Fair models (lavender on sage)
░░░░░░░░░░░░░░░░░░░░░░░░░░░ Healthcare for all (in every shade)

Head of Data @ FoXX Health

Leading healthcare equity initiatives
Building bias detection frameworks
Shipping models that don't exclude

All in intentionally mismatched pastels

THE PASTEL BREATHING:

Inhale   ░░░░░░░░░░ Soft hypothesis      [Pink wonder]
Hold     ▒▒▒▒▒▒▒▒▒▒ Gentle validation    [Yellow patience]  
Exhale   ▓▓▓▓▓▓▓▓▓▓ Mint production      [Green growth]
Pause    ██████████ Sage impact          [Deep but soft]

This is my palette.
Mismatched but intentional.
Soft but never weak.

I'm always interested in conversations about pattern discovery, ethical AI, or why medical algorithms think everyone is a 70kg male. Also happy to discuss career transitions, the beauty of well-documented code, or why pastel color schemes are objectively superior.

Topics That Light Me Up

Hidden patterns in data • Building fair AI systems • Healthcare innovation
Statistical methods that actually work • Open source collaboration
Making complex things simple • Women in tech • Ethical technology
That one bug that took three days to find (it was a typo)

░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Curious about patterns?
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ Interested in fairness?
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Want to build together?
████████████████████████████████████ Let's make AI fair

THE FINAL COMPRESSION

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║                                                                                   ║
║  COMPRESSION COMPLETE:                                                            ║
║                                                                                   ║
║  Career = ∫(Pink → Blue → Mint → Purpose)dt                                       ║
║                                                                                   ║
║  Every color combination intentionally unexpected                                 ║
║  Pink on mint, blue on blush, lavender on sage                                    ║
║  Mismatched but never unintentional                                               ║
║                                                                                   ║
║  I find patterns in noise                                                         ║
║  I fix bias in algorithms                                                         ║
║  I do it all in mismatched pastels                                                ║
║                                                                                   ║
║  Because different is powerful                                                    ║
║  And unexpected is memorable                                                      ║
║                                                                                   ║
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%%{init: {'theme': 'base', 'themeVariables': {'primaryColor':'#FFE0F5','fontSize':'14px'}}}%%
pie title Where My Code Lives
    "Python (Data Science)" : 45
    "Python (ML/AI)" : 30
    "JavaScript (Viz)" : 15
    "R (Stats)" : 8
    "Shell (Automation)" : 2

I write code like I used to write lab reports: obsessively documented, thoroughly tested, and with enough comments that future-me won't hate past-me.